In this paper, we present a matching method that can improve the classification performance of a fuzzy decision tree (FDT). This method takes into consideration prediction strength of leave nodes of a fuzzy decision t...
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ISBN:
(纸本)0780378652
In this paper, we present a matching method that can improve the classification performance of a fuzzy decision tree (FDT). This method takes into consideration prediction strength of leave nodes of a fuzzy decision tree by combining true degrees (CF) of fuzzy rules, generated from a fuzzy decision tree, with membership degrees of antecedent parts of rules when applied to cases for classification. We illustrate the importance of CF through an example. An experiment shows by using this method, we can obtain more accurate results of classification when compared to the original method and to those obtained using the C5.0 decision tree.
Perhaps due to its existentiality, the fact that simulated virtual humans give no impression of having an existence beyond their interactions with human users is often ignored in intelligent agent systems for virtual ...
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The Minimax Probability machine Classification (MPMC) framework [Lanckriet et al., 2002] builds classifiers by minimizing the maximum probability of misclassification, and gives direct estimates of the probabilistic a...
The Minimax Probability machine Classification (MPMC) framework [Lanckriet et al., 2002] builds classifiers by minimizing the maximum probability of misclassification, and gives direct estimates of the probabilistic accuracy bound Ω. The only assumptions that MPMC makes is that good estimates of means and covariance matrices of the classes exist. However, as with Support Vector machines, MPMC is computationally expensive and requires extensive cross validation experiments to choose kernels and kernel parameters that give good performance. In this paper we address the computational cost of MPMC by proposing an algorithm that constructs nonlinear sparse MPMC (SMPMC) models by incrementally adding basis functions (i.e. kernels) one at a time – greedily selecting the next one that maximizes the accuracy bound Ω. SMPMC automatically chooses both kernel parameters and feature weights without using computationally expensive cross validation. Therefore the SMPMC algorithm simultaneously addresses the problem of kernel selection and feature selection (i.e. feature weighting), based solely on maximizing the accuracy bound Ω. Experimental results indicate that we can obtain reliable bounds Ω, as well as test set accuracies that are comparable to state of the art classification algorithms.
learning from structured data is becoming increasingly important. However, most prior work on kernel methods has focused on learning from attribute-value data. Only recently have researchers started investigating kern...
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ISBN:
(纸本)9783540005674
learning from structured data is becoming increasingly important. However, most prior work on kernel methods has focused on learning from attribute-value data. Only recently have researchers started investigating kernels for structured data. This paper describes how kernel definitions can be simplified by identifying the structure of the data and how kernels can be defined on this structure. We propose a kernel for structured data, prove that it is positive definite, and show how it can be adapted in practical applications.
We present a novel method for approximate inference in Bayesian models and regularized risk functionals. It is based on the propagation of mean and variance derived from the Laplace approximation of conditional probab...
We present a novel method for approximate inference in Bayesian models and regularized risk functionals. It is based on the propagation of mean and variance derived from the Laplace approximation of conditional probabilities in factorizing distributions, much akin to Minka's Expectation Propagation. In the jointly normal case, it coincides with the latter and belief propagation, whereas in the general case, it provides an optimization strategy containing Support Vector chunking, the Bayes Committee machine, and Gaussian Process chunking as special cases.
The ability to identify the mineral composition of rocks and soils is an important tool for the exploration of geological sites. Even though expert knowledge is commonly used for this task, it is desirable to create a...
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Agents that learn on-line with partial instance memory reserve some of the previously encountered examples for use in future training episodes. We extend our previous work by combining our method for selecting extreme...
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A class of artificial neural networks with a two-layer feedback topology to solve nonlinear discrete dynamic optimization problems is developed. Generalized recurrent neuron models are introduced. A direct method to a...
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A class of artificial neural networks with a two-layer feedback topology to solve nonlinear discrete dynamic optimization problems is developed. Generalized recurrent neuron models are introduced. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. A comparative analysis of the computational requirements is made. The analysis shows advantages of this approach as compared to the standard dynamic programming algorithm. The technique has been applied to several important optimization problems, such as shortest path and control optimal problems.
A key practical obstacle in applying support vector machines to many large-scale data mining tasks is that SVM training time generally scales quadratically (or worse) in the number of examples or support vectors. This...
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ISBN:
(纸本)1581132336
A key practical obstacle in applying support vector machines to many large-scale data mining tasks is that SVM training time generally scales quadratically (or worse) in the number of examples or support vectors. This complexity is further compounded when a specific SVM training is but one of many, such as in Leave-One-Out-Cross-Validation (LOOCV) for determining optimal SVM parameters or as in wrapper-based feature selection. In this paper we explore new techniques for reducing the amortized cost of each such SVM training, by seeding successive SVM trainings with the results of previous similar trainings.
Constructive induction, which is defined to be the process of constructing new and useful features from existing ones, has been extensively studied in the literature. Since the number of possible high order features f...
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Constructive induction, which is defined to be the process of constructing new and useful features from existing ones, has been extensively studied in the literature. Since the number of possible high order features for any given learning problem is exponential in the number of input attributes (where the order of a feature is defined to be the number of attributes of which it is composed), the main problem faced by constructive induction is in selecting which features to use out of this exponentially large set of potential features. For any feature set chosen the desirable characteristics are minimality and generalization performance. The paper uses a combination of genetic algorithms and linear programming techniques to generate feature sets. The genetic algorithm searches for higher order features while at the same time seeking to minimize the size of the feature set in order to produce a feature set with good generalization accuracy. The features chosen are used as inputs to a high order perceptron network which is trained with an interior point linear programming method. Performance on a holdout set is used in conjunction with complexity penalization in order to insure that the final feature set generated by the genetic algorithm does not overfit the training data.
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